%pip install -q --no-warn-conflicts malariagen_data
import warnings
warnings.simplefilter(action='ignore', category=Warning)
import malariagen_data
import numpy as np
import pandas as pd
import os
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 165.1/165.1 kB 2.8 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.2/3.2 MB 38.0 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 35.8/35.8 MB 32.0 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.5/7.5 MB 82.9 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.0/4.0 MB 84.4 MB/s eta 0:00:00 Preparing metadata (setup.py) ... done ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 302.5/302.5 kB 29.5 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 141.1/141.1 kB 14.5 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 24.4/24.4 MB 17.3 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.1/8.1 MB 61.7 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 210.2/210.2 kB 21.8 MB/s eta 0:00:00 Preparing metadata (setup.py) ... done ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.6/8.6 MB 62.3 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 71.5 MB/s eta 0:00:00 Building wheel for dash-cytoscape (setup.py) ... done Building wheel for asciitree (setup.py) ... done
mount google drive
!pip install nbconvert
try:
from google.colab import drive
drive.mount("drive")
except ImportError:
pass
Drive already mounted at drive; to attempt to forcibly remount, call drive.mount("drive", force_remount=True).
define directory within google drive as data cache
results_dir = "drive/MyDrive/malariagen_data_cache"
ag3 = malariagen_data.Ag3(results_cache=results_dir)
ag3
| MalariaGEN Ag3 API client | |
|---|---|
| Please note that data are subject to terms of use, for more information see the MalariaGEN website or contact support@malariagen.net. See also the Ag3 API docs. | |
| Storage URL | gs://vo_agam_release/ |
| Data releases available | 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10 |
| Results cache | /content/drive/MyDrive/malariagen_data_cache |
| Cohorts analysis | 20240418 |
| AIM analysis | 20220528 |
| Site filters analysis | dt_20200416 |
| Software version | malariagen_data 11.0.0 |
| Client location | Utah, United States (Google Cloud us-west3) |
define sample sets
sample_sets = ["AG1000G-BF-A", "AG1000G-BF-B", "AG1000G-BF-C"]
sample_query="cohort_admin2_year == ['BF-09_Houet_gamb_2014', 'BF-09_Houet_gamb_2012']"
contig="2L"
ag3.plot_h12_calibration(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
)
window=2500
ag3.plot_h12_gwss(
contig=contig,
window_size=window,
sample_sets=sample_sets,
sample_query=sample_query,
)
# Parameters
output_file_path = os.path.join(results_dir, 'H12counts_gambiae_Chr2L.tsv')
outliers_file_path = os.path.join(results_dir, 'H12counts_95qoutliers_gambiae_Chr2L.tsv')
# Perform H12 calculation
h12counts = ag3.h12_gwss(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
window_size=window,
)
# Initialize an empty list to store the results
results = []
# Process h12counts based on its actual type and structure
if isinstance(h12counts, tuple):
window_positions, h12_values = h12counts
# Convert window positions to integers
window_positions = window_positions.astype(int)
else:
print("Unexpected h12counts format")
# Calculate start and end positions based on the window size
start_positions = window_positions - (window // 2)
end_positions = window_positions + (window // 2)
# Save the results to the output file in tabular format with headers and contig name
with open(output_file_path, 'w') as file:
# Write the headers
file.write("chr\tstart\tend\tmid\tH12\n")
# Write data
for start, end, mid, h12 in zip(start_positions, end_positions, window_positions, h12_values):
file.write(f"{contig}\t{start}\t{end}\t{mid}\t{h12}\n")
print(f"Results saved to {output_file_path}")
# Load the results into a DataFrame
df = pd.read_csv(output_file_path, delimiter='\t')
# Compute the 95th percentile for H12 values
threshold = np.percentile(df['H12'], 95)
# Filter the DataFrame to get the top 5% H12 values
outliers_df = df[df['H12'] >= threshold]
# Save the outliers to a new file
outliers_df.to_csv(outliers_file_path, sep='\t', index=False)
print(f"Outliers saved to {outliers_file_path}")
Results saved to drive/MyDrive/malariagen_data_cache/H12counts_gambiae_Chr2L.tsv Outliers saved to drive/MyDrive/malariagen_data_cache/H12counts_95qoutliers_gambiae_Chr2L.tsv
sample_query="cohort_admin2_year == ['BF-09_Houet_gamb_2014', 'BF-09_Houet_gamb_2012']"
contig="2R"
ag3.plot_h12_calibration(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
)
window=1000
ag3.plot_h12_gwss(
contig=contig,
window_size=window,
sample_sets=sample_sets,
sample_query=sample_query,
)
# Parameters
output_file_path = os.path.join(results_dir, 'H12counts_gambiae_Chr2R.tsv')
outliers_file_path = os.path.join(results_dir, 'H12counts_95qoutliers_gambiae_Chr2R.tsv')
# Perform H12 calculation
h12counts = ag3.h12_gwss(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
window_size=window,
)
# Initialize an empty list to store the results
results = []
# Process h12counts based on its actual type and structure
if isinstance(h12counts, tuple):
window_positions, h12_values = h12counts
# Convert window positions to integers
window_positions = window_positions.astype(int)
else:
print("Unexpected h12counts format")
# Calculate start and end positions based on the window size
start_positions = window_positions - (window // 2)
end_positions = window_positions + (window // 2)
# Save the results to the output file in tabular format with headers and contig name
with open(output_file_path, 'w') as file:
# Write the headers
file.write("chr\tstart\tend\tmid\tH12\n")
# Write data
for start, end, mid, h12 in zip(start_positions, end_positions, window_positions, h12_values):
file.write(f"{contig}\t{start}\t{end}\t{mid}\t{h12}\n")
print(f"Results saved to {output_file_path}")
# Load the results into a DataFrame
df = pd.read_csv(output_file_path, delimiter='\t')
# Compute the 95th percentile for H12 values
threshold = np.percentile(df['H12'], 95)
# Filter the DataFrame to get the top 5% H12 values
outliers_df = df[df['H12'] >= threshold]
# Save the outliers to a new file
outliers_df.to_csv(outliers_file_path, sep='\t', index=False)
print(f"Outliers saved to {outliers_file_path}")
Results saved to drive/MyDrive/malariagen_data_cache/H12counts_gambiae_Chr2R.tsv Outliers saved to drive/MyDrive/malariagen_data_cache/H12counts_95qoutliers_gambiae_Chr2R.tsv
sample_query="cohort_admin2_year == ['BF-09_Houet_gamb_2014', 'BF-09_Houet_gamb_2012']"
contig="3L"
ag3.plot_h12_calibration(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
)
window=1000
ag3.plot_h12_gwss(
contig=contig,
window_size=window,
sample_sets=sample_sets,
sample_query=sample_query,
)
# Parameters
output_file_path = os.path.join(results_dir, 'H12counts_gambiae_Chr3L.tsv')
outliers_file_path = os.path.join(results_dir, 'H12counts_95qoutliers_gambiae_Chr3L.tsv')
# Perform H12 calculation
h12counts = ag3.h12_gwss(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
window_size=window,
)
# Initialize an empty list to store the results
results = []
# Process h12counts based on its actual type and structure
if isinstance(h12counts, tuple):
window_positions, h12_values = h12counts
# Convert window positions to integers
window_positions = window_positions.astype(int)
else:
print("Unexpected h12counts format")
# Calculate start and end positions based on the window size
start_positions = window_positions - (window // 2)
end_positions = window_positions + (window // 2)
# Save the results to the output file in tabular format with headers and contig name
with open(output_file_path, 'w') as file:
# Write the headers
file.write("chr\tstart\tend\tmid\tH12\n")
# Write data
for start, end, mid, h12 in zip(start_positions, end_positions, window_positions, h12_values):
file.write(f"{contig}\t{start}\t{end}\t{mid}\t{h12}\n")
print(f"Results saved to {output_file_path}")
# Load the results into a DataFrame
df = pd.read_csv(output_file_path, delimiter='\t')
# Compute the 95th percentile for H12 values
threshold = np.percentile(df['H12'], 95)
# Filter the DataFrame to get the top 5% H12 values
outliers_df = df[df['H12'] >= threshold]
# Save the outliers to a new file
outliers_df.to_csv(outliers_file_path, sep='\t', index=False)
print(f"Outliers saved to {outliers_file_path}")
Results saved to drive/MyDrive/malariagen_data_cache/H12counts_gambiae_Chr3L.tsv Outliers saved to drive/MyDrive/malariagen_data_cache/H12counts_95qoutliers_gambiae_Chr3L.tsv
sample_query="cohort_admin2_year == ['BF-09_Houet_gamb_2014', 'BF-09_Houet_gamb_2012']"
contig="3R"
ag3.plot_h12_calibration(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
)
window=1000
ag3.plot_h12_gwss(
contig=contig,
window_size=window,
sample_sets=sample_sets,
sample_query=sample_query,
)
# Parameters
output_file_path = os.path.join(results_dir, 'H12counts_gambiae_Chr3R.tsv')
outliers_file_path = os.path.join(results_dir, 'H12counts_95qoutliers_gambiae_Chr3R.tsv')
# Perform H12 calculation
h12counts = ag3.h12_gwss(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
window_size=window,
)
# Initialize an empty list to store the results
results = []
# Process h12counts based on its actual type and structure
if isinstance(h12counts, tuple):
window_positions, h12_values = h12counts
# Convert window positions to integers
window_positions = window_positions.astype(int)
else:
print("Unexpected h12counts format")
# Calculate start and end positions based on the window size
start_positions = window_positions - (window // 2)
end_positions = window_positions + (window // 2)
# Save the results to the output file in tabular format with headers and contig name
with open(output_file_path, 'w') as file:
# Write the headers
file.write("chr\tstart\tend\tmid\tH12\n")
# Write data
for start, end, mid, h12 in zip(start_positions, end_positions, window_positions, h12_values):
file.write(f"{contig}\t{start}\t{end}\t{mid}\t{h12}\n")
print(f"Results saved to {output_file_path}")
# Load the results into a DataFrame
df = pd.read_csv(output_file_path, delimiter='\t')
# Compute the 95th percentile for H12 values
threshold = np.percentile(df['H12'], 95)
# Filter the DataFrame to get the top 5% H12 values
outliers_df = df[df['H12'] >= threshold]
# Save the outliers to a new file
outliers_df.to_csv(outliers_file_path, sep='\t', index=False)
print(f"Outliers saved to {outliers_file_path}")
Results saved to drive/MyDrive/malariagen_data_cache/H12counts_gambiae_Chr3R.tsv Outliers saved to drive/MyDrive/malariagen_data_cache/H12counts_95qoutliers_gambiae_Chr3R.tsv
sample_query="cohort_admin2_year == ['BF-09_Houet_gamb_2014', 'BF-09_Houet_gamb_2012']"
contig="X"
ag3.plot_h12_calibration(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
)
window=2500
ag3.plot_h12_gwss(
contig=contig,
window_size=window,
sample_sets=sample_sets,
sample_query=sample_query,
)
# Parameters
output_file_path = os.path.join(results_dir, 'H12counts_gambiae_ChrX.tsv')
outliers_file_path = os.path.join(results_dir, 'H12counts_95qoutliers_gambiae_ChrX.tsv')
# Perform H12 calculation
h12counts = ag3.h12_gwss(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
window_size=window,
)
# Initialize an empty list to store the results
results = []
# Process h12counts based on its actual type and structure
if isinstance(h12counts, tuple):
window_positions, h12_values = h12counts
# Convert window positions to integers
window_positions = window_positions.astype(int)
else:
print("Unexpected h12counts format")
# Calculate start and end positions based on the window size
start_positions = window_positions - (window // 2)
end_positions = window_positions + (window // 2)
# Save the results to the output file in tabular format with headers and contig name
with open(output_file_path, 'w') as file:
# Write the headers
file.write("chr\tstart\tend\tmid\tH12\n")
# Write data
for start, end, mid, h12 in zip(start_positions, end_positions, window_positions, h12_values):
file.write(f"{contig}\t{start}\t{end}\t{mid}\t{h12}\n")
print(f"Results saved to {output_file_path}")
# Load the results into a DataFrame
df = pd.read_csv(output_file_path, delimiter='\t')
# Compute the 95th percentile for H12 values
threshold = np.percentile(df['H12'], 95)
# Filter the DataFrame to get the top 5% H12 values
outliers_df = df[df['H12'] >= threshold]
# Save the outliers to a new file
outliers_df.to_csv(outliers_file_path, sep='\t', index=False)
print(f"Outliers saved to {outliers_file_path}")
Results saved to drive/MyDrive/malariagen_data_cache/H12counts_gambiae_ChrX.tsv Outliers saved to drive/MyDrive/malariagen_data_cache/H12counts_95qoutliers_gambiae_ChrX.tsv
sample_query="cohort_admin2_year == ['BF-09_Houet_colu_2014', 'BF-09_Houet_colu_2012']"
contig="2L"
ag3.plot_h12_calibration(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
)
window=1000
ag3.plot_h12_gwss(
contig=contig,
window_size=window,
sample_sets=sample_sets,
sample_query=sample_query,
)
# Parameters
output_file_path = os.path.join(results_dir, 'H12counts_coluzzii_Chr2L.tsv')
outliers_file_path = os.path.join(results_dir, 'H12counts_95qoutliers_coluzzii_Chr2L.tsv')
# Perform H12 calculation
h12counts = ag3.h12_gwss(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
window_size=window,
)
# Initialize an empty list to store the results
results = []
# Process h12counts based on its actual type and structure
if isinstance(h12counts, tuple):
window_positions, h12_values = h12counts
# Convert window positions to integers
window_positions = window_positions.astype(int)
else:
print("Unexpected h12counts format")
# Calculate start and end positions based on the window size
start_positions = window_positions - (window // 2)
end_positions = window_positions + (window // 2)
# Save the results to the output file in tabular format with headers and contig name
with open(output_file_path, 'w') as file:
# Write the headers
file.write("chr\tstart\tend\tmid\tH12\n")
# Write data
for start, end, mid, h12 in zip(start_positions, end_positions, window_positions, h12_values):
file.write(f"{contig}\t{start}\t{end}\t{mid}\t{h12}\n")
print(f"Results saved to {output_file_path}")
# Load the results into a DataFrame
df = pd.read_csv(output_file_path, delimiter='\t')
# Compute the 95th percentile for H12 values
threshold = np.percentile(df['H12'], 95)
# Filter the DataFrame to get the top 5% H12 values
outliers_df = df[df['H12'] >= threshold]
# Save the outliers to a new file
outliers_df.to_csv(outliers_file_path, sep='\t', index=False)
print(f"Outliers saved to {outliers_file_path}")
Results saved to drive/MyDrive/malariagen_data_cache/H12counts_coluzzii_Chr2L.tsv Outliers saved to drive/MyDrive/malariagen_data_cache/H12counts_95qoutliers_coluzzii_Chr2L.tsv
sample_query="cohort_admin2_year == ['BF-09_Houet_colu_2014', 'BF-09_Houet_colu_2012']"
contig="2R"
ag3.plot_h12_calibration(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
)
window=1000
ag3.plot_h12_gwss(
contig=contig,
window_size=window,
sample_sets=sample_sets,
sample_query=sample_query,
)
# Parameters
output_file_path = os.path.join(results_dir, 'H12counts_coluzzii_Chr2R.tsv')
outliers_file_path = os.path.join(results_dir, 'H12counts_95qoutliers_coluzzii_Chr2R.tsv')
# Perform H12 calculation
h12counts = ag3.h12_gwss(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
window_size=window,
)
# Initialize an empty list to store the results
results = []
# Process h12counts based on its actual type and structure
if isinstance(h12counts, tuple):
window_positions, h12_values = h12counts
# Convert window positions to integers
window_positions = window_positions.astype(int)
else:
print("Unexpected h12counts format")
# Calculate start and end positions based on the window size
start_positions = window_positions - (window // 2)
end_positions = window_positions + (window // 2)
# Save the results to the output file in tabular format with headers and contig name
with open(output_file_path, 'w') as file:
# Write the headers
file.write("chr\tstart\tend\tmid\tH12\n")
# Write data
for start, end, mid, h12 in zip(start_positions, end_positions, window_positions, h12_values):
file.write(f"{contig}\t{start}\t{end}\t{mid}\t{h12}\n")
print(f"Results saved to {output_file_path}")
# Load the results into a DataFrame
df = pd.read_csv(output_file_path, delimiter='\t')
# Compute the 95th percentile for H12 values
threshold = np.percentile(df['H12'], 95)
# Filter the DataFrame to get the top 5% H12 values
outliers_df = df[df['H12'] >= threshold]
# Save the outliers to a new file
outliers_df.to_csv(outliers_file_path, sep='\t', index=False)
print(f"Outliers saved to {outliers_file_path}")
Results saved to drive/MyDrive/malariagen_data_cache/H12counts_coluzzii_Chr2R.tsv Outliers saved to drive/MyDrive/malariagen_data_cache/H12counts_95qoutliers_coluzzii_Chr2R.tsv
sample_query="cohort_admin2_year == ['BF-09_Houet_colu_2014', 'BF-09_Houet_colu_2012']"
contig="3L"
ag3.plot_h12_calibration(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
)
window=1000
ag3.plot_h12_gwss(
contig=contig,
window_size=window,
sample_sets=sample_sets,
sample_query=sample_query,
)
# Parameters
output_file_path = os.path.join(results_dir, 'H12counts_coluzzii_Chr3L.tsv')
outliers_file_path = os.path.join(results_dir, 'H12counts_95qoutliers_coluzzii_Chr3L.tsv')
# Perform H12 calculation
h12counts = ag3.h12_gwss(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
window_size=window,
)
# Initialize an empty list to store the results
results = []
# Process h12counts based on its actual type and structure
if isinstance(h12counts, tuple):
window_positions, h12_values = h12counts
# Convert window positions to integers
window_positions = window_positions.astype(int)
else:
print("Unexpected h12counts format")
# Calculate start and end positions based on the window size
start_positions = window_positions - (window // 2)
end_positions = window_positions + (window // 2)
# Save the results to the output file in tabular format with headers and contig name
with open(output_file_path, 'w') as file:
# Write the headers
file.write("chr\tstart\tend\tmid\tH12\n")
# Write data
for start, end, mid, h12 in zip(start_positions, end_positions, window_positions, h12_values):
file.write(f"{contig}\t{start}\t{end}\t{mid}\t{h12}\n")
print(f"Results saved to {output_file_path}")
# Load the results into a DataFrame
df = pd.read_csv(output_file_path, delimiter='\t')
# Compute the 95th percentile for H12 values
threshold = np.percentile(df['H12'], 95)
# Filter the DataFrame to get the top 5% H12 values
outliers_df = df[df['H12'] >= threshold]
# Save the outliers to a new file
outliers_df.to_csv(outliers_file_path, sep='\t', index=False)
print(f"Outliers saved to {outliers_file_path}")
Results saved to drive/MyDrive/malariagen_data_cache/H12counts_coluzzii_Chr3L.tsv Outliers saved to drive/MyDrive/malariagen_data_cache/H12counts_95qoutliers_coluzzii_Chr3L.tsv
sample_query="cohort_admin2_year == ['BF-09_Houet_colu_2014', 'BF-09_Houet_colu_2012']"
contig="3R"
ag3.plot_h12_calibration(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
)
window=1000
ag3.plot_h12_gwss(
contig=contig,
window_size=window,
sample_sets=sample_sets,
sample_query=sample_query,
)
# Parameters
output_file_path = os.path.join(results_dir, 'H12counts_coluzzii_Chr3R.tsv')
outliers_file_path = os.path.join(results_dir, 'H12counts_95qoutliers_coluzzii_Chr3R.tsv')
# Perform H12 calculation
h12counts = ag3.h12_gwss(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
window_size=window,
)
# Initialize an empty list to store the results
results = []
# Process h12counts based on its actual type and structure
if isinstance(h12counts, tuple):
window_positions, h12_values = h12counts
# Convert window positions to integers
window_positions = window_positions.astype(int)
else:
print("Unexpected h12counts format")
# Calculate start and end positions based on the window size
start_positions = window_positions - (window // 2)
end_positions = window_positions + (window // 2)
# Save the results to the output file in tabular format with headers and contig name
with open(output_file_path, 'w') as file:
# Write the headers
file.write("chr\tstart\tend\tmid\tH12\n")
# Write data
for start, end, mid, h12 in zip(start_positions, end_positions, window_positions, h12_values):
file.write(f"{contig}\t{start}\t{end}\t{mid}\t{h12}\n")
print(f"Results saved to {output_file_path}")
# Load the results into a DataFrame
df = pd.read_csv(output_file_path, delimiter='\t')
# Compute the 95th percentile for H12 values
threshold = np.percentile(df['H12'], 95)
# Filter the DataFrame to get the top 5% H12 values
outliers_df = df[df['H12'] >= threshold]
# Save the outliers to a new file
outliers_df.to_csv(outliers_file_path, sep='\t', index=False)
print(f"Outliers saved to {outliers_file_path}")
Results saved to drive/MyDrive/malariagen_data_cache/H12counts_coluzzii_Chr3R.tsv Outliers saved to drive/MyDrive/malariagen_data_cache/H12counts_95qoutliers_coluzzii_Chr3R.tsv
sample_query="cohort_admin2_year == ['BF-09_Houet_colu_2014', 'BF-09_Houet_colu_2012']"
contig="X"
ag3.plot_h12_calibration(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
)
window=5000
ag3.plot_h12_gwss(
contig=contig,
window_size=window,
sample_sets=sample_sets,
sample_query=sample_query,
)
# Parameters
output_file_path = os.path.join(results_dir, 'H12counts_coluzzii_ChrX.tsv')
outliers_file_path = os.path.join(results_dir, 'H12counts_95qoutliers_coluzzii_ChrX.tsv')
# Perform H12 calculation
h12counts = ag3.h12_gwss(
contig=contig,
sample_sets=sample_sets,
sample_query=sample_query,
window_size=window,
)
# Initialize an empty list to store the results
results = []
# Process h12counts based on its actual type and structure
if isinstance(h12counts, tuple):
window_positions, h12_values = h12counts
# Convert window positions to integers
window_positions = window_positions.astype(int)
else:
print("Unexpected h12counts format")
# Calculate start and end positions based on the window size
start_positions = window_positions - (window // 2)
end_positions = window_positions + (window // 2)
# Save the results to the output file in tabular format with headers and contig name
with open(output_file_path, 'w') as file:
# Write the headers
file.write("chr\tstart\tend\tmid\tH12\n")
# Write data
for start, end, mid, h12 in zip(start_positions, end_positions, window_positions, h12_values):
file.write(f"{contig}\t{start}\t{end}\t{mid}\t{h12}\n")
print(f"Results saved to {output_file_path}")
# Load the results into a DataFrame
df = pd.read_csv(output_file_path, delimiter='\t')
# Compute the 95th percentile for H12 values
threshold = np.percentile(df['H12'], 95)
# Filter the DataFrame to get the top 5% H12 values
outliers_df = df[df['H12'] >= threshold]
# Save the outliers to a new file
outliers_df.to_csv(outliers_file_path, sep='\t', index=False)
print(f"Outliers saved to {outliers_file_path}")
Results saved to drive/MyDrive/malariagen_data_cache/H12counts_coluzzii_ChrX.tsv Outliers saved to drive/MyDrive/malariagen_data_cache/H12counts_95qoutliers_coluzzii_ChrX.tsv
%cd /content/drive/MyDrive/Colab\ Notebooks/
/content/drive/MyDrive/Colab Notebooks
# Step 2: Convert the notebook to HTML
import os
# Define the filename
notebook_filename = "drive/MyDrive/Colab\ Notebooks/H12stats_Burkina.ipynb"
html_filename = notebook_filename.replace(".ipynb", ".html")
# Save the notebook
!jupyter nbconvert --to html "{notebook_filename}"
# Step 3: Download the HTML file
from google.colab import files
# Download the HTML file
files.download(html_filename)
[NbConvertApp] WARNING | pattern 'drive/MyDrive/Colab\\ Notebooks/H12stats_Burkina.ipynb' matched no files
This application is used to convert notebook files (*.ipynb)
to various other formats.
WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.
Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
<cmd> --help-all
--debug
set log level to logging.DEBUG (maximize logging output)
Equivalent to: [--Application.log_level=10]
--show-config
Show the application's configuration (human-readable format)
Equivalent to: [--Application.show_config=True]
--show-config-json
Show the application's configuration (json format)
Equivalent to: [--Application.show_config_json=True]
--generate-config
generate default config file
Equivalent to: [--JupyterApp.generate_config=True]
-y
Answer yes to any questions instead of prompting.
Equivalent to: [--JupyterApp.answer_yes=True]
--execute
Execute the notebook prior to export.
Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
Write notebook output to stdout instead of files.
Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
--inplace
Run nbconvert in place, overwriting the existing notebook (only
relevant when converting to notebook format)
Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
Clear output of current file and save in place,
overwriting the existing notebook.
Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--no-prompt
Exclude input and output prompts from converted document.
Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
Exclude input cells and output prompts from converted document.
This mode is ideal for generating code-free reports.
Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]
--allow-chromium-download
Whether to allow downloading chromium if no suitable version is found on the system.
Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
--disable-chromium-sandbox
Disable chromium security sandbox when converting to PDF..
Equivalent to: [--WebPDFExporter.disable_sandbox=True]
--show-input
Shows code input. This flag is only useful for dejavu users.
Equivalent to: [--TemplateExporter.exclude_input=False]
--embed-images
Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.
Equivalent to: [--HTMLExporter.embed_images=True]
--sanitize-html
Whether the HTML in Markdown cells and cell outputs should be sanitized..
Equivalent to: [--HTMLExporter.sanitize_html=True]
--log-level=<Enum>
Set the log level by value or name.
Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
Default: 30
Equivalent to: [--Application.log_level]
--config=<Unicode>
Full path of a config file.
Default: ''
Equivalent to: [--JupyterApp.config_file]
--to=<Unicode>
The export format to be used, either one of the built-in formats
['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf']
or a dotted object name that represents the import path for an
``Exporter`` class
Default: ''
Equivalent to: [--NbConvertApp.export_format]
--template=<Unicode>
Name of the template to use
Default: ''
Equivalent to: [--TemplateExporter.template_name]
--template-file=<Unicode>
Name of the template file to use
Default: None
Equivalent to: [--TemplateExporter.template_file]
--theme=<Unicode>
Template specific theme(e.g. the name of a JupyterLab CSS theme distributed
as prebuilt extension for the lab template)
Default: 'light'
Equivalent to: [--HTMLExporter.theme]
--sanitize_html=<Bool>
Whether the HTML in Markdown cells and cell outputs should be sanitized.This
should be set to True by nbviewer or similar tools.
Default: False
Equivalent to: [--HTMLExporter.sanitize_html]
--writer=<DottedObjectName>
Writer class used to write the
results of the conversion
Default: 'FilesWriter'
Equivalent to: [--NbConvertApp.writer_class]
--post=<DottedOrNone>
PostProcessor class used to write the
results of the conversion
Default: ''
Equivalent to: [--NbConvertApp.postprocessor_class]
--output=<Unicode>
overwrite base name use for output files.
can only be used when converting one notebook at a time.
Default: ''
Equivalent to: [--NbConvertApp.output_base]
--output-dir=<Unicode>
Directory to write output(s) to. Defaults
to output to the directory of each notebook. To recover
previous default behaviour (outputting to the current
working directory) use . as the flag value.
Default: ''
Equivalent to: [--FilesWriter.build_directory]
--reveal-prefix=<Unicode>
The URL prefix for reveal.js (version 3.x).
This defaults to the reveal CDN, but can be any url pointing to a copy
of reveal.js.
For speaker notes to work, this must be a relative path to a local
copy of reveal.js: e.g., "reveal.js".
If a relative path is given, it must be a subdirectory of the
current directory (from which the server is run).
See the usage documentation
(https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)
for more details.
Default: ''
Equivalent to: [--SlidesExporter.reveal_url_prefix]
--nbformat=<Enum>
The nbformat version to write.
Use this to downgrade notebooks.
Choices: any of [1, 2, 3, 4]
Default: 4
Equivalent to: [--NotebookExporter.nbformat_version]
Examples
--------
The simplest way to use nbconvert is
> jupyter nbconvert mynotebook.ipynb --to html
Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].
> jupyter nbconvert --to latex mynotebook.ipynb
Both HTML and LaTeX support multiple output templates. LaTeX includes
'base', 'article' and 'report'. HTML includes 'basic', 'lab' and
'classic'. You can specify the flavor of the format used.
> jupyter nbconvert --to html --template lab mynotebook.ipynb
You can also pipe the output to stdout, rather than a file
> jupyter nbconvert mynotebook.ipynb --stdout
PDF is generated via latex
> jupyter nbconvert mynotebook.ipynb --to pdf
You can get (and serve) a Reveal.js-powered slideshow
> jupyter nbconvert myslides.ipynb --to slides --post serve
Multiple notebooks can be given at the command line in a couple of
different ways:
> jupyter nbconvert notebook*.ipynb
> jupyter nbconvert notebook1.ipynb notebook2.ipynb
or you can specify the notebooks list in a config file, containing::
c.NbConvertApp.notebooks = ["my_notebook.ipynb"]
> jupyter nbconvert --config mycfg.py
To see all available configurables, use `--help-all`.
--------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) <ipython-input-68-bbca913fce8c> in <cell line: 15>() 13 14 # Download the HTML file ---> 15 files.download(html_filename) /usr/local/lib/python3.10/dist-packages/google/colab/files.py in download(filename) 223 if not _os.path.exists(filename): 224 msg = 'Cannot find file: {}'.format(filename) --> 225 raise FileNotFoundError(msg) # pylint: disable=undefined-variable 226 227 comm_manager = _IPython.get_ipython().kernel.comm_manager FileNotFoundError: Cannot find file: drive/MyDrive/Colab\ Notebooks/H12stats_Burkina.html